## [1] "Show relationships amongst variables visually"

## [1] "A-B scatter plot"
## [1] "A plot shows a level change from the 200th data point, can be controlled by a dummy variable"

## [1] "B plot looks fairly random/stochastic - avoiding tests of stationarity as the visual looks representative"

## [1] "C plot has a strong outlier at point 201"

## [1] "Autocorrelation for A - potentially two lags may be fitting, but none used as a useful model was found using the interaction effect (see below)"

#Removing outlier
content <- content[-c(201),]

#Creating Dummy Variable
content$D <- c(rep(0, 200), rep(1, NROW(content)-200))
#Modeling with interactions with Dummy given the break and phase shift in underlying series after 200th point
model <- lm(C ~ A * B * D, data=content)
summary(model)
## 
## Call:
## lm(formula = C ~ A * B * D, data = content)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -125.899   -3.114    1.597    7.604   35.144 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   9.6114     1.6381   5.867  1.2e-08 ***
## A             1.3560     0.5124   2.646  0.00858 ** 
## B            -5.3259     0.4310 -12.358  < 2e-16 ***
## D            -2.3636     6.1447  -0.385  0.70077    
## A:B           0.7792     0.1435   5.429  1.2e-07 ***
## A:D           0.9344     0.7797   1.198  0.23172    
## B:D          22.2786     2.0070  11.100  < 2e-16 ***
## A:B:D        -6.9617     0.2521 -27.619  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.69 on 291 degrees of freedom
## Multiple R-squared:  0.9465, Adjusted R-squared:  0.9452 
## F-statistic: 735.5 on 7 and 291 DF,  p-value: < 2.2e-16
#Residuals appear fairly stable
resi <- model$residuals
plot(resi)